Biological Systems Engineering Lab
Living organisms developed in nature through the evolution process are equipped with supremely skilled and sophisticated biological functions that cannot be realized with current engineering techniques. Analysis of these mechanisms may lead to not only elucidation of biological functions but also development of a wide variety of novel engineering systems.
From the viewpoint of a scientist approaching the secrets of living organisms and from that of an engineer developing machinery useful for human kind, the members of Biological Systems Engineering laboratory work on a wide variety of projects to analyze the characteristics of biological functions from theoretical and experimental approaches employing engineering techniques aiming to find new principles peculiar to biological systems, and develop novel medical/welfare apparatuses and industrial devices by applying the elucidated principles.
Through such research activities, the students can learn in-depth knowledge about biological systems based on electricity, electronics, systems and information engineering foundation allowing themselves to become creative engineers capable of seeking a new principle and expanding it into new fields.
Five research themes
There are still a lot of unknown functions and mechanisms hidden in the biological system. If we can elucidate and utilize them from engineering standpoint,then there is a possibility of creating new technologies to carve out the future of the 21st century. The Biological Systems Engineering Laboratory categorizes the broad research field of biological systems into five major research themes in order to explore specific research projects under each theme, and further functionally coordinate and fuse each theme to create novel research fields.
Biological signal analysis and its application to human interfaces
We develop novel signal processing algorithms that enablethe interpretation of human motions, intentions, and physiological/psychological states contained in biological signals, such as myoelectric signals, electroencephalograms, and electrocardiograms, as well as create robotic interfaces and medical welfare equipment.
Biomechanical analysis and its application to human-machine system design
We model human sensory/motor functions from electrical and electronicperspectives based on experimentally measured data, and develop novel movement support systems and next-generation automobile control systems by incorporating modeled human characteristics.
Statistical structure of neural networks based on novel machine learning algorithms
We propose new machine learning algorithms and neural networks based on probabilistic statistical theory and applythese to the development oflearning and control technologiesfor robots, medical welfare equipment, and medical data classificationtechnology.
Brain function/neural network modeling and artificial life models
Focusing on functions such as locomotion generation, sensation, perception, learning, and judgment, we model brain functions from an engineering viewpoint using artificial neural networks. Ultimately, we aim to model and analyze higher brain functions, especially social brain functions that understand the minds of others and live harmoniously, and Kansei that involves nonverbal, unconscious, and intuitive sensibilities. We also develop artificial life form models based on biological knowledge using the constructed brain models.
Biometric information mining technology and medical support systems
We are engaged in the research and development of novel medical support systems and medical devices through medicine-engineering collaborations by utilizing electric and electronic systems and information engineering technologies, such as biomechanical analysis technology, biological signal analysis technology, machine learning technology, and biological simulation technology that were developed in the Biological Systems Engineering laboratory.
Our research results have been published in scientific journals, books, conference proceedings, patent, etc.. The numbers of publications the lab produced are shown as follows
(as of October 25, 2021):
Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
Kazuma Sakamoto, Zu Soh, Michiyo Suzuki, Yuichi Iino, and Toshio Tsuji
Scientific Reports, volume 11, Article number: 13737, doi.org/10.1038/s41598-021-92690-2, Published online: 02 July 2021. (SCI, IF=4.379)
The right hemisphere is important for driving-related cognitive function after stroke
Koji Shimonaga, Seiji Hama, Toshio Tsuji, Kazumasa Yoshimura, Shinya Nishino, Akiko Yanagawa, Zu Soh, Toshinori Matsushige, Tatsuya Mizoue, Keiichi Onoda, Hidehisa Yamashita, Shigeto Yamawaki, and Kaoru Kurisu
Neurosurgical Review, vol. 44, pp.977-985, doi.org/10.1007/s10143-020-01272-9, Published online: 11 March, 2020, 2021 (SCI, IF=2.654)
Prediction of blood pressure change during surgical incision under opioid analgesia using sympathetic response evoking threshold
Satoshi Kamiya, Ryuji Nakamura, Noboru Saeki, Takashi Kondo, Hirotsugu Miyoshi, Soushi Narasaki, Atsushi Morio, Masashi Kawamoto, Harutoyo Hirano, Toshio Tsuji, and Yasuo M Tsutsumi
Scientific Reports, volume 11, Article number: 9558, doi.org/10.1038/s41598-021-87636-7, Published online: 5 May 2021. (SCI, IF=3.998)
Cardiorespiratory Synchronisation and Systolic Blood Pressure Correlation of Peripheral Arterial Stiffness During Endoscopic Thoracic Sympathectomy
Toshifumi Muneyasu, Harutoyo Hirano, Akira Furui, Zu Soh, Ryuji Nakamura, Noboru Saeki, Yoshiyuki Okada, Masashi Kawamoto, Masao Yoshizumi, Atsuo Yoshino, Takafumi Sasaoka, Shigeto Yamawaki, and Toshio Tsuji
Scientific Reports, volume 11, Article number: 5966, doi.org/10.1038/s41598-021-85299-y, Published online: 16 March 2021. (SCI, IF=3.998)
Peripheral Arterial Stiffness During Electrocutaneous Stimulation is Positively Correlated with Pain-related Brain Activity and Subjective Pain Intensity: An fMRI Study
Toshio Tsuji, Fumiya Arikuni, Takafumi Sasaoka, Shin Suyama, Takashi Akiyoshi, Zu Soh, Harutoyo Hirano, Ryuji Nakamura, Noboru Saeki, Masashi Kawamoto, Masao Yoshizumi, Atsuo Yoshino, and Shigeto Yamawaki
Scientific Reports, volume 11, Article number: 4425, doi.org/10.1038/s41598-021-83833-6, Published online: 24 February 2021. (SCI, IF=3.998)
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures
Akira Furui, Ryota Onishi, Akihito Takeuchi, Tomoyuki Akiyama, and Toshio Tsuji
IEEE Transactions on Biomedical Engineering, Volume: 68, Issue: 2, pp. 515-525, Digital Object Identifier: 10.1109/TBME.2020.3006246, Publication Date: FEBRUARY 2021 (SCI, IF = 4.424)
Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies
Chiara Tacchino, Martina Impagliazzo, Erika Maggi, Marta Bertamino, Isa Blanchi, Francesca Campone, Paola Durand, Marco Fato, Psiche Giannoni, Riccardo Iandolo, Massimiliano Izzo, Pietro Morasso, Paolo Moretti, Luca Ramenghi, Keisuke Shima, Koji Shimatani, Toshio Tsuji, Sara Uccella, Nicolo Zanardi, and Maura Casadio
Computer Methods and Programs in Biomedicine, Volume 199, 105838, pp.1-17, Available online 21 November 2020, February 2021 (SCI, IF=3.632)
Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
Satoshi Miyamoto, Zu Soh, Shigeyuki Okahara, Akira Furui, Taiichi Takasaki, Keijiro Katayama, Shinya Takahashi, and Toshio Tsuji
Scientific Reports, volume 11, Article number: 549, doi.org/10.1038/s41598-020-80810-3, Published online: 12 January 2021. (SCI, IF=3.998)
Human Hand Impedance Characteristics during Maintained Posture in Multi-Joint Arm Movements
T. Tsuji, P. Morasso, K. Goto, and K. Ito
Biological Cybernetics, Vol.72, pp.475-485, 1995.
A Log-Linearized Gaussian Mixture Network and Its Application to EEG Pattern Classification
T. Tsuji, O. Fukuda, H. Ichinobe, and M. Kaneko
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 29, No. 1, pp. 60-72, February 1999.
A Recurrent Log-linearized Gaussian Mixture Network
T. Tsuji, N. Bu, M. Kaneko, and O. Fukuda
IEEE Transactions on Neural Networks, Vol.14, No.2, pp.304-316, March 2003.
A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions
O. Fukuda, T. Tsuji, M. Kaneko and A. Otsuka
IEEE Transactions on Robotics and Automation, Vol.19, No.2, pp.210-222, April 2003.
Quantitative Evaluation of Pain during Electrocutaneous Stimulation using a Log-Linearized Peripheral Arterial Viscoelastic Model
H. Matsubara, H. Hirano, H. Hirano, Z. Soh, R. Nakamura, N. Saeki, M. Kawamoto, M. Yoshizumi, A. Yoshino, T. Sasaoka, S. Yamawaki, and T. Tsuji
Scientific Reports, volume 8, Article number: 3091, doi:10.1038/s41598-018-21223-11, Published online: 15 February 2018.
Continuous Blood Viscosity Monitoring System for Cardiopulmonary Bypass Applications
S. Okahara, Z. Soh, S. Miyamoto, H. Takahashi, S. Takahashi, T. Sueda, and T. Tsuji
IEEE Transactions on Biomedical Engineering, Vol.64, No.7, pp. 1503-1512, DOI:10.1109/TBME.2016.2610968, JULY 2017.
Assessment of Lower-limb Vascular Endothelial Function Based on Enclosed Zone Flow-mediated Dilation
H. Hirano, R. Takama, R. Matsumoto, H. Tanaka, H. Hirano, Z. Soh, T. Ukawa, T. Takayanagi, H. Morimoto, R. Nakamura, N. Saeki, H. Hashimoto, S. Matsui, S. Kishimoto, N. Oda, M. Kajikawa, T. Maruhashi, M. Kawamoto, M. Yoshizumi, Y. Higashi, and T. Tsuji
Scientific Reports, volume 8, Article number: 9263, doi:10.1038/s41598-018-27392-3, Published online: 18 June 2018.
A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans
Z. Soh, K. Sakamoto , M. Suzuki , Y. Iino, and T. Tsuji
Scientific Reports, volume 8, Article number: 17190, doi:10.1038/s41598-018-35157-1, Published online: 21 November 2018.
A Scale Mixture-based Stochastic Model of Surface EMG Signals with Variable Variances
A. Furui, H. Hayashi, and T. Tsuji
IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2019.2895683, Date of Publication: 28 January 2019.
A myoelectric prosthetic hand with muscle synergy-based motion determination and impedance model-based biomimetic control
A. Furui, S. Eto, K. Nakagaki, K. Shimada, G. Nakamura, A. Masuda, T. Chin, and T. Tsuji
Science Robotics, Vol. 4, Issue 31, eaaw6339, DOI: 10.1126/scirobotics.eaaw6339, 26 June 2019.
Markerless Measurement and Evaluation of General Movements in Infants
T. Tsuji, S. Nakashima, H. Hayashi, Z. Soh, A. Furui, T. Shibanoki, K. Shima, and K. Shimatani
Scientific Reports, volume 10, Article number: 1422, doi:10.1038/s41598-020-57580-z, Published online: 29 January 2020.